本文選題：公交車到站時間 + 預(yù)測��； 參考：《北京交通大學(xué)》2015年碩士論文

【摘要】：優(yōu)先發(fā)展公共交通,是轉(zhuǎn)變交通增長方式、提高資源利用效率、減少交通污染和緩解交通擁堵的有效方法,也是我國城市交通發(fā)展的主體思路。發(fā)展公共交通的關(guān)鍵在于為乘客提供良好的公共交通服務(wù),公交車到站時間預(yù)測有利于出行者合理安排行程及公交車輛的實時調(diào)配,是提高公交服務(wù)水平的有效手段。 論文實現(xiàn)了公交車輛運行的GPS位置數(shù)據(jù)與公交線路數(shù)據(jù)的匹配,通過插值得到公交車每秒的位置信息,且根據(jù)公交車到達站點的先后順序,提取了前后車關(guān)系,并系統(tǒng)地分析了公交車的運行特性及公交車到站時間的影響因素。 在此基礎(chǔ)上,針對現(xiàn)有研究中很少明確地研究公交車在站點的�？繒r間及公交車通過交叉口的時間,且較少考慮道路交通流狀態(tài)對公交車運行時間的影響這兩個問題,論文首先針對路段運行時間及車站�？繒r間,提出了基于ε-支持向量機回歸(ε-SVR)的無交叉口公交車到站時間預(yù)測模型,利用遺傳算法優(yōu)化模型參數(shù)及輸入變量,并基于北京市三環(huán)300路內(nèi)公交車的實例數(shù)據(jù),分別按照工作日高峰(7：00～9：00/17：00～19：00)、平峰,以及周末3個時段,驗證了模型的有效性。預(yù)測結(jié)果表明,通過引入路段檢測器數(shù)據(jù),能有效降低模型的預(yù)測誤差。上述三個時段模型的平均相對預(yù)測誤差最大分別能減少1.55%、1.50%和1.25%。 論文進一步針對公交車通過交叉口的時間,提出了考慮交叉口的公交車到站時間預(yù)測模型,并基于VISSIM仿真,驗證了模型的有效性。VISSIM仿真結(jié)果表明,對于路段的交通流處于暢通和緩行狀態(tài)時,交叉口預(yù)測模型都能獲得較高的預(yù)測精度,而對于擁擠狀態(tài),由于可能存在公交車在停車線前二次排隊的現(xiàn)象,所以平均相對預(yù)測誤差相對較大。
[Abstract]:Giving priority to the development of public transport is an effective way to change the way of traffic growth, improve the efficiency of resource utilization, reduce traffic pollution and alleviate traffic congestion. It is also the main idea of the development of urban traffic in China. The key to the development of public transportation is to provide good public service for passengers, and the prediction of bus arrival time is beneficial to travel. It is an effective way to improve the level of public transport service by arranging the itinerary and arranging the traffic in real time.
The paper realizes the matching between the GPS position data of the bus running and the bus line data, gets the position information of the bus per second through interpolation, and extracts the relationship between the front and back vehicles according to the order of the bus arrival site, and systematically analyzes the operating characteristics of the bus and the influencing factors of the bus arrival time.
On this basis, we seldom study the time of the stop time of the bus at the station and the time of the bus passing through the intersection in the existing research, and take less consideration to the two problems that the road traffic flow state affects the bus running time. First, the paper puts forward the epsilon support direction for the section running time and the station stop time. By using the genetic algorithm to optimize the model parameters and input variables of the bus arrival time without intersection (-SVR), and based on the example data of the three ring 300 road buses in Beijing, the validity of the model is verified according to the peak of working day (7:00 ~ 9:00/17:00 to 19:00), flat peak, and 3 periods of the weekend. The prediction results show that the prediction error of the model can be reduced effectively by introducing the link detector data. The maximum average relative prediction error of the three period models can be reduced by 1.55%, 1.50% and 1.25%. respectively.
The paper further aims at the time of the bus crossing through the intersection, and puts forward the bus arrival time prediction model considering the intersection. Based on the VISSIM simulation, the validity of the model is verified by.VISSIM simulation results. The results show that the intersection forecast model can obtain higher prediction precision for the traffic flow in the smooth and slow state of the section. For the crowded condition, the average relative prediction error is relatively large because there may be two queues in front of the parking line.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U491.17

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本文編號：2065246